ISI Journals

This research investigates advanced methods for cyberattack detection in high-noise environments, a significant challenge in modern cybersecurity. High-noise environments, characterized by a large volume of legitimate and illegitimate network traffic, can obscure malicious activities, making detection difficult. By employing advanced machine learning techniques and signal processing methods, this study aims to enhance the accuracy and efficiency of cyberattack detection systems. The findings underscore the importance of robust cybersecurity measures and provide valuable insights for developing more effective detection strategies in noisy settings. This study addresses the challenge of detecting cyberattacks in high-noise environments, where the volume of irrelevant or benign data can obscure genuine threats. We propose an advanced framework that enhances cybersecurity measures through the integration of machine learning algorithms and noise-filtering techniques to improve attack detection accuracy. By applying sophisticated anomaly detection models and feature selection methods, our approach effectively differentiates between malicious activities and benign noise, thereby reducing false positives and improving overall detection rates. The framework is tested against various high-noise datasets, demonstrating significant improvements in identifying and responding to cyber threats. This research provides a robust solution for enhancing cybersecurity in complex and data-rich environments, contributing to more effective and reliable protection against cyberattacks.